Beam steering radar with adjustable long-range radar mode for autonomous vehicles

ABSTRACT

Examples disclosed herein relate to a beam steering radar for use in an autonomous vehicle. The beam steering radar has a radar module with at least one beam steering antenna, a transceiver, and a controller that can cause the transceiver to perform, using the at least one beam steering antenna, a first scan of a field-of-view (FoV) with a first number of chirps in a first radio frequency (RF) signal and a second scan of the FoV with a second number of chirps in a second RF signal. The radar module also has a perception module having a machine learning-trained classifier that can detect objects in a path and surrounding environment of the autonomous vehicle based on the first number of chirps in the first RF signal and classify the objects based on the second number of chirps in the second RF signal.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Prov. Appl. No. 62/869,899,titled “BEAM STEERING RADAR WITH AN ADJUSTABLE LONG RANGE MODE FOR USEIN AUTONOMOUS VEHICLES,” filed on Jul. 2, 2019, which is incorporated byreference herein in its entirety.

BACKGROUND

Autonomous driving is quickly moving from the realm of science fictionto becoming an achievable reality. Already in the market areAdvanced-Driver Assistance Systems (“ADAS”) that automate, adapt andenhance vehicles for safety and better driving. The next step will bevehicles that increasingly assume control of driving functions such assteering, accelerating, braking and monitoring the surroundingenvironment and driving conditions to respond to events, such aschanging lanes or speed when needed to avoid traffic, crossingpedestrians, animals, and so on. The requirements for object and imagedetection are critical and specify the time required to capture data,process it and turn it into action. All this while ensuring accuracy,consistency and cost optimization.

An aspect of making this work is the ability to detect and classifyobjects in the surrounding environment at the same or possibly evenbetter level as humans. Humans are adept at recognizing and perceivingthe world around them with an extremely complex human visual system thatessentially has two main functional parts: the eye and the brain. Inautonomous driving technologies, the eye may include a combination ofmultiple sensors, such as camera, radar, and lidar, while the brain mayinvolve multiple artificial intelligence, machine learning and deeplearning systems. The goal is to have full understanding of a dynamic,fast-moving environment in real time and human-like intelligence to actin response to changes in the environment.

BRIEF DESCRIPTION OF THE DRAWINGS

The present application may be more fully appreciated in connection withthe following detailed description taken in conjunction with theaccompanying drawings, which are not drawn to scale and in which likereference characters refer to like parts throughout, and wherein:

FIG. 1 illustrates an example environment in which a beam steering radarwith an adjustable long-range mode in an autonomous vehicle is used todetect and identify objects;

FIG. 2 is a schematic diagram of an autonomous driving system for anautonomous vehicle in accordance with various examples;

FIG. 3 is a schematic diagram of a beam steering radar system as in FIG.2 in accordance with various examples;

FIG. 4 illustrates the antenna elements of the receive and guardantennas of FIG. 3 in more detail in accordance with various examples;

FIG. 5 illustrates an example radar signal and its associated scanparameters in more detail;

FIG. 6 is a flowchart for operating a beam steering radar in anadjustable long-range mode in accordance with various examples;

FIGS. 7A-B illustrate an example radar beam transmitted by a beamsteering radar implemented as in FIG. 3 and in accordance with variousexamples;

FIGS. 8A-B illustrate example scan parameters to generate the radar beamof FIGS. 7A-B in accordance with various examples;

FIG. 9 shows a range doppler map and a frequency spectrum for an echoreceived by a beam steering radar implemented as in FIG. 3 in accordancewith various examples;

FIG. 10 are range doppler maps for echo received from signalstransmitted with a different number of chirps in accordance with variousexamples; and

FIG. 11 illustrate frequency spectrum graphs for echoes received fromsignals transmitted with a different number of chirps in accordance withvarious examples.

DETAILED DESCRIPTION

A beam steering radar with an adjustable long-range mode for use inautonomous vehicles is disclosed. The beam steering radar incorporatesat least one beam steering antenna that is dynamically controlled suchas to change its electrical or electromagnetic configuration to enablebeam steering. In various examples, the beam steering radar operates asa long-range radar (“LRR”) to enable a narrow, directed beam at a longdistance and having high gain for a high-speed operation to detectobjects. Once the objects are detected, the radar adjusts its LRR modeto increase the number of chirps in the radar signal and improve thevelocity estimation for the detected objects. The dynamic control isimplemented with processing engines which, upon identifying objects inthe vehicle's field-of-view (FoV), informs the beam steering radar whereto steer its beams and focus on areas and objects of interest byadjusting its radar scan parameters. The objects of interest may includestructural elements in the vehicle's FoV such as roads, walls, buildingsand road center medians, as well as other vehicles, pedestrians,bystanders, cyclists, plants, trees, animals and so on.

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology may bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a thorough understandingof the subject technology. However, the subject technology is notlimited to the specific details set forth herein and may be practicedusing one or more implementations. In one or more instances, structuresand components are shown in block diagram form in order to avoidobscuring the concepts of the subject technology. In other instances,well-known methods and structures may not be described in detail toavoid unnecessarily obscuring the description of the examples. Also, theexamples may be used in combination with each other.

FIG. 1 illustrates an example environment in which a beam steering radarwith an adjustable long-range mode in an autonomous vehicle is used todetect and identify objects. Ego vehicle 100 is an autonomous vehiclewith a beam steering radar system 106 for transmitting a radar signal toscan a FoV or specific area. As described in more detail below, theradar signal is transmitted according to a set of scan parameters thatcan be adjusted to result in multiple transmission beams 118. The scanparameters may include, among others, the total angle of the scannedarea defining the FoV, the beam width or the scan angle of eachincremental transmission beam, the number of chirps in the radar signal,the chirp time, the chirp segment time, the chirp slope, and so on. Theentire FoV or a portion of it can be scanned by a compilation of suchtransmission beams 118, which may be in successive adjacent scanpositions or in a specific or random order. Note that the term FoV isused herein in reference to the radar transmissions and does not implyan optical FoV with unobstructed views. The scan parameters may alsoindicate the time interval between these incremental transmission beams,as well as start and stop angle positions for a full or partial scan.

In various examples, the ego vehicle 100 may also have other perceptionsensors, such as camera 102 and lidar 104. These perception sensors arenot required for the ego vehicle 100, but may be useful in augmentingthe object detection capabilities of the beam steering radar 106. Camerasensor 102 may be used to detect visible objects and conditions and toassist in the performance of various functions. The lidar sensor 104 canalso be used to detect objects and provide this information to adjustcontrol of the vehicle. This information may include information such ascongestion on a highway, road conditions, and other conditions thatwould impact the sensors, actions or operations of the vehicle. Camerasensors are currently used in Advanced Driver Assistance Systems(“ADAS”) to assist drivers in driving functions such as parking (e.g.,in rear view cameras). Cameras can capture texture, color and contrastinformation at a high level of detail, but similar to the human eye,they are susceptible to adverse weather conditions and variations inlighting. Camera 102 may have a high resolution but cannot resolveobjects beyond 50 meters.

Lidar sensors typically measure the distance to an object by calculatingthe time taken by a pulse of light to travel to an object and back tothe sensor. When positioned on top of a vehicle, a lidar sensor canprovide a 360° 3D view of the surrounding environment. Other approachesmay use several lidars at different locations around the vehicle toprovide the full 360° view. However, lidar sensors such as lidar 104 arestill prohibitively expensive, bulky in size, sensitive to weatherconditions and are limited to short ranges (typically <150-200 meters).Radars, on the other hand, have been used in vehicles for many years andoperate in all-weather conditions. Radars also use far less processingthan the other types of sensors and have the advantage of detectingobjects behind obstacles and determining the speed of moving objects.When it comes to resolution, lidars' laser beams are focused on smallareas, have a smaller wavelength than RF signals, and can achieve around0.25 degrees of resolution.

In various examples and as described in more detail below, the beamsteering radar 106 can provide a 360° true 3D vision and human-likeinterpretation of the ego vehicle's path and surrounding environment.The beam steering radar 106 can shape and steer RF beams in alldirections in a 360° FoV with at least one beam steering antenna andrecognize objects quickly and with a high degree of accuracy over a longrange of around 300 meters or more. The short-range capabilities ofcamera 102 and lidar 104 along with the long-range capabilities of radar106 enable a sensor fusion module 108 in ego vehicle 100 to enhance itsobject detection and identification.

As illustrated, beam steering radar 106 can detect both vehicle 120 at afar range (e.g., >250 m) as well as bus 122 at a short range (e.g., <100m). Detecting both in a short amount of time and with enough range andvelocity resolution is imperative for full autonomy of driving functionsof the ego vehicle. Radar 106 has an adjustable LRR mode that enablesthe detection of long-range objects in a very short time to then focuson obtaining finer velocity resolution for the detected vehicles.Although not described herein, radar 106 is capable oftime-alternatively reconfiguring between LRR and short-range radar(“SRR”) modes. The SRR mode enables a wide beam with lower gain, but canmake quick decisions to avoid an accident, assist in parking anddowntown travel, and capture information about a broad area of theenvironment. The LRR mode enables a narrow, directed beam and longdistance, having high gain; this is powerful for high speedapplications, and where longer processing time allows for greaterreliability. The adjustable LRR mode disclosed herein uses a reducednumber of chirps (e.g., 5, 10, 15, or 20) to reduce the chirp segmenttime by up to 75%, guaranteeing a fast beam scanning rate that iscritical for successful object detection and autonomous vehicleperformance. Excessive dwell time for each beam position may cause blindzones, and the adjustable LRR mode ensures that fast object detectioncan occur at long range while maintaining the antenna gain, transmitpower and desired SNR for the radar operation.

Attention is now directed to FIG. 2, which illustrates a schematicdiagram of an autonomous driving system for an ego vehicle in accordancewith various examples. Autonomous driving system 200 is a system for usein an ego vehicle that provides some or full automation of drivingfunctions. The driving functions may include, for example, steering,accelerating, braking, and monitoring the surrounding environment anddriving conditions to respond to events, such as changing lanes or speedwhen needed to avoid traffic, crossing pedestrians, animals, and so on.The autonomous driving system 200 includes a beam steering radar system202 and other sensor systems such as camera 204, lidar 206,infrastructure sensors 208, environmental sensors 210, operationalsensors 212, user preference sensors 214, and other sensors 216.Autonomous driving system 200 also includes a communications module 218,a sensor fusion module 220, a system controller 222, a system memory224, and a vehicle-to-vehicle (V2V) communications module 226. It isappreciated that this configuration of autonomous driving system 200 isan example configuration and not meant to be limiting to the specificstructure illustrated in FIG. 2. Additional systems and modules notshown in FIG. 2 may be included in autonomous driving system 200.

In various examples, beam steering radar 202 with adjustable LRR modeincludes at least one beam steering antenna for providing dynamicallycontrollable and steerable beams that can focus on one or multipleportions of a 360° FoV of the vehicle. The beams radiated from the beamsteering antenna are reflected back from objects in the vehicle's pathand surrounding environment and received and processed by the radar 202to detect and identify the objects. Radar 202 includes a perceptionmodule that is trained to detect and identify objects and control theradar module as desired. Camera sensor 204 and lidar 206 may also beused to identify objects in the path and surrounding environment of theego vehicle, albeit at a much lower range.

Infrastructure sensors 208 may provide information from infrastructurewhile driving, such as from a smart road configuration, bill boardinformation, traffic alerts and indicators, including traffic lights,stop signs, traffic warnings, and so forth. This is a growing area, andthe uses and capabilities derived from this information are immense.Environmental sensors 210 detect various conditions outside, such astemperature, humidity, fog, visibility, precipitation, among others.Operational sensors 212 provide information about the functionaloperation of the vehicle. This may be tire pressure, fuel levels, brakewear, and so forth. The user preference sensors 214 may be configured todetect conditions that are part of a user preference. This may betemperature adjustments, smart window shading, etc. Other sensors 216may include additional sensors for monitoring conditions in and aroundthe vehicle.

In various examples, the sensor fusion module 220 optimizes thesevarious functions to provide an approximately comprehensive view of thevehicle and environments. Many types of sensors may be controlled by thesensor fusion module 220. These sensors may coordinate with each otherto share information and consider the impact of one control action onanother system. In one example, in a congested driving condition, anoise detection module (not shown) may identify that there are multipleradar signals that may interfere with the vehicle. This information maybe used by a perception module in radar 202 to adjust the radar's scanparameters so as to avoid these other signals and minimize interference.

In another example, environmental sensor 210 may detect that the weatheris changing, and visibility is decreasing. In this situation, the sensorfusion module 220 may determine to configure the other sensors toimprove the ability of the vehicle to navigate in these new conditions.The configuration may include turning off camera or lidar sensors204-206 or reducing the sampling rate of these visibility-based sensors.This effectively places reliance on the sensor(s) adapted for thecurrent situation. In response, the perception module configures theradar 202 for these conditions as well. For example, the radar 202 mayreduce the beam width to provide a more focused beam, and thus a finersensing capability.

In various examples, the sensor fusion module 220 may send a directcontrol to radar 202 based on historical conditions and controls. Thesensor fusion module 220 may also use some of the sensors within system200 to act as feedback or calibration for the other sensors. In thisway, an operational sensor 212 may provide feedback to the perceptionmodule and/or the sensor fusion module 220 to create templates, patternsand control scenarios. These are based on successful actions or may bebased on poor results, where the sensor fusion module 220 learns frompast actions.

Data from sensors 202-216 may be combined in sensor fusion module 220 toimprove the target detection and identification performance ofautonomous driving system 200. Sensor fusion module 220 may itself becontrolled by system controller 222, which may also interact with andcontrol other modules and systems in the vehicle. For example, systemcontroller 222 may turn the different sensors 202-216 on and off asdesired, or provide instructions to the vehicle to stop upon identifyinga driving hazard (e.g., deer, pedestrian, cyclist, or another vehiclesuddenly appearing in the vehicle's path, flying debris, etc.).

All modules and systems in autonomous driving system 200 communicatewith each other through communication module 218. Autonomous drivingsystem 200 also includes system memory 224, which may store informationand data (e.g., static and dynamic data) used for operation of system200 and the ego vehicle using system 200. V2V communications module 226is used for communication with other vehicles. The V2V communicationsmay also include information from other vehicles that is invisible tothe user, driver, or rider of the vehicle, and may help vehiclescoordinate to avoid an accident.

FIG. 3 illustrates a schematic diagram of a beam steering radar systemwith adjustable LRR mode as in FIG. 2 in accordance with variousexamples. Beam steering radar 300 is a “digital eye” with true 3D visionand capable of a human-like interpretation of the world. The “digitaleye” and human-like interpretation capabilities are provided by two mainmodules: radar module 302 and a perception engine 304. Radar module 302is capable of both transmitting RF signals within a FoV and receivingthe reflections of the transmitted signals as they reflect off ofobjects in the FoV. With the use of analog beamforming in radar module302, a single transmit and receive chain can be used effectively to forma directional, as well as a steerable, beam. A transceiver 306 in radarmodule 302 is adapted to generate signals for transmission through aseries of transmit antennas 308 as well as manage signals receivedthrough a series of receive antennas 310-314. Beam steering within theFoV is implemented with phase shifter (“PS”) circuits 316-318 coupled tothe transmit antennas 308 on the transmit chain and PS circuits 320-324coupled to the receive antennas 310-314 on the receive chain,respectively.

The use of PS circuits 316-318 and 320-324 enables separate control ofthe phase of each element in the transmit and receive antennas. Unlikeearly passive architectures, the beam is steerable not only to discreteangles but to any angle (i.e., from 0° to 360°) within the FoV usingactive beamforming antennas. A multiple element antenna can be used withan analog beamforming architecture where the individual antenna elementsmay be combined or divided at the port of the single transmit or receivechain without additional hardware components or individual digitalprocessing for each antenna element. Further, the flexibility ofmultiple element antennas allows narrow beam width for transmit andreceive. The antenna beam width decreases with an increase in the numberof antenna elements. A narrow beam improves the directivity of theantenna and provides the radar 300 with a significantly longer detectionrange.

The major challenge with implementing analog beam steering is to designPSs to operate at 77 GHz. PS circuits 316-318 and 320-324 solve thisproblem with a reflective PS design implemented with a distributedvaractor network currently built using GaAs materials. Each PS circuit316-318 and 320-324 has a series of PSs, with each PS coupled to anantenna element to generate a phase shift value of anywhere from 0° to360° for signals transmitted or received by the antenna element. The PSdesign is scalable in future implementations to SiGe and CMOS, bringingdown the PS cost to meet specific demands of customer applications. EachPS circuit 316-318 and 320-324 is controlled by a Field ProgrammableGate Array (“FPGA”) 326, which provides a series of voltages to the PSsin each PS circuit that results in a series of phase shifts.

In various examples, a voltage value is applied to each PS in the PScircuits 316-318 and 320-324 to generate a given phase shift and providebeam steering. The voltages applied to the PSs in PS circuits 316-318and 320-324 are stored in Look-up Tables (“LUTs”) in the FPGA 306. TheseLUTs are generated by an antenna calibration process that determineswhich voltages to apply to each PS to generate a given phase shift undereach operating condition. Note that the PSs in PS circuits 316-318 and320-324 are capable of generating phase shifts at a very high resolutionof less than one degree. This enhanced control over the phase allows thetransmit and receive antennas in radar module 302 to steer beams with avery small step size, improving the capability of the radar 300 toresolve closely located targets at small angular resolution.

In various examples, the transmit antennas 308 and the receive antennas310-314 may be a meta-structure antenna, a phase array antenna, or anyother antenna capable of radiating RF signals in millimeter wavefrequencies. A meta-structure, as generally defined herein, is anengineered structure capable of controlling and manipulating incidentradiation at a desired direction based on its geometry. Variousconfigurations, shapes, designs and dimensions of the antennas 308-314may be used to implement specific designs and meet specific constraints.

The transmit chain in radar 300 starts with the transceiver 306generating RF signals to prepare for transmission over-the-air by thetransmit antennas 308. The RF signals may be, for example,Frequency-Modulated Continuous Wave (“FMCW”) signals. An FMCW signalenables the radar 300 to determine both the range to an object and theobject's velocity by measuring the differences in phase or frequencybetween the transmitted signals and the received/reflected signals orechoes. Within FMCW formats, there are a variety of waveform patternsthat may be used, including sinusoidal, triangular, sawtooth,rectangular and so forth, each having advantages and purposes.

Once the FMCW signals are generated by the transceiver 306, they areprovided to power amplifiers (“PAs”) 328-332. Signal amplification isneeded for the FMCW signals to reach the long ranges desired for objectdetection, as the signals attenuate as they radiate by the transmitantennas 308. From the PAs 328-332, the signals are divided anddistributed through feed networks 334-336, which form a power dividersystem to divide an input signal into multiple signals, one for eachelement of the transmit antennas 308. The feed networks 334-336 maydivide the signals so power is equally distributed among them oralternatively, so power is distributed according to another scheme, inwhich the divided signals do not all receive the same power. Each signalfrom the feed networks 334-336 is then input into a PS in PS circuits316-318, where they are phase shifted based on voltages generated by theFPGA 326 under the direction of microcontroller 338 and then transmittedthrough transmit antennas 308.

Microcontroller 338 determines which phase shifts to apply to the PSs inPS circuits 316-318 according to a desired scanning mode based on roadand environmental scenarios. Microcontroller 338 also determines thescan parameters for the transceiver to apply at its next scan. The scanparameters may be determined at the direction of one of the processingengines 350, such as at the direction of perception engine 304.Depending on the objects detected, the perception engine 304 mayinstruct the microcontroller 338 to adjust the scan parameters at a nextscan to focus on a given area of the FoV or to steer at a differentdirection.

In various examples and as described in more detail below, radar 300operates in a LRR mode to enable a narrow, directed beam at a longdistance and having high gain for a high-speed operation to detectobjects with a reduced number of chirps. In this mode, both transmitantennas 308 and receive antennas 310-314 scan a complete FoV with smallincremental steps. Even though the FoV may be limited by systemparameters due to increased side lobes as a function of the steeringangle, radar 300 can detect objects over a significant area for along-range radar. The range of angles to be scanned on either side ofboresight as well as the step size between steering angles/phase shiftscan be dynamically varied based on the driving environment.

To improve performance of an autonomous vehicle, such as an ego vehicle,driving through an urban environment, the scan range can be increased tokeep monitoring the intersections and curbs to detect vehicles,pedestrians, bicyclists and so on. This wide scan range may deterioratethe frame rate (revisit rate), but this is acceptable as urbanenvironments may generally involve low velocity driving scenarios. For ahigh-speed freeway scenario where the frame rate is critical, a higherframe rate can be maintained by reducing the scan range. On a freeway, afew degrees of beam scanning on either side of boresight would sufficefor a long-range object detection and tracking. Once objects aredetected at long range, radar 300 increases the number of chirps in theradar signal and improves the velocity estimation for the detectedobjects.

Objects are detected with radar 300 by reflections or echoes that arereceived at the series of receive antennas 310-314, which are directedby PS circuits 320-324. Low Noise Amplifiers (“LNAs”) are positionedbetween receive antennas 310-314 and PS circuits 320-324, which includePSs similar to the PSs in PS circuits 316-318. For receive operation, PScircuits 310-324 create phase differentials between radiating elementsin the receive antennas 310-314 to compensate for the time delay ofreceived signals between radiating elements due to spatialconfigurations. Receive phase-shifting, also referred to as analogbeamforming, combines the received signals for aligning echoes toidentify the location, or position of a detected object. That is, phaseshifting aligns the received signals that arrive at different times ateach of the radiating elements in receive antennas 310-314. Similar toPS circuits 316-318 on the transmit chain, PS circuits 320-324 arecontrolled by FPGA 326, which provides the voltages to each PS togenerate the desired phase shift. FPGA 326 also provides bias voltagesto the LNAs 338-342.

The receive chain then combines the signals received at receive antennas312 at combination network 344, from which the combined signalspropagate to the transceiver 306. Note that as illustrated, combinationnetwork 344 generates two combined signals 346-348, with each signalcombining signals from a number of elements in the receive antennas 312.In one example, receive antennas 312 include 48 radiating elements andeach combined signal 346-348 combines signals received by 24 of the 48elements. Other examples may include 8, 16, 24, 32, and soon, dependingon the desired configuration. The higher the number of antenna elements,the narrower the beam width.

Note also that the signals received at receive antennas 310 and 314 godirectly from PS circuits 320 and 324 to the transceiver 306. Receiveantennas 310 and 314 are guard antennas that generate a radiationpattern separate from the main beams received by the 48-element receiveantenna 312. Guard antennas 310 and 314 are implemented to effectivelyeliminate side-lobe returns from objects. The goal is for the guardantennas 310 and 314 to provide a gain that is higher than the sidelobes and therefore enable their elimination or reduce their presencesignificantly. Guard antennas 310 and 314 effectively act as a side lobefilter.

Once the received signals are received by transceiver 306, they areprocessed by processing engines 350. Processing engines 350 includeperception engine 304 which detects and identifies objects in thereceived signal with neural network and artificial intelligencetechniques, database 352 to store historical and other information forradar 300, and a Digital Signal Processing (“DSP”) engine 354 with anAnalog-to-Digital Converter (“ADC”) module to convert the analog signalsfrom transceiver 306 into digital signals that can be processed todetermine angles of arrival and other valuable information for thedetection and identification of objects by perception engine 304. In oneor more implementations, DSP engine 354 may be integrated with themicrocontroller 338 or the transceiver 306.

Radar 300 also includes a Graphical User Interface (“GUI”) 358 to enableconfiguration of scan parameters such as the total angle of the scannedarea defining the FoV, the beam width or the scan angle of eachincremental transmission beam, the number of chirps in the radar signal,the chirp time, the chirp slope, the chirp segment time, and so on asdesired. In addition, radar 300 has a temperature sensor 360 for sensingthe temperature around the vehicle so that the proper voltages from FPGA326 may be used to generate the desired phase shifts. The voltagesstored in FPGA 326 are determined during calibration of the antennasunder different operating conditions, including temperature conditions.A database 362 may also be used in radar 300 to store radar and otheruseful data.

Attention is now directed to FIG. 4, which shows the antenna elements ofthe receive and guard antennas of FIG. 3 in more detail. Receive antenna400 has a number of radiating elements 402 creating receive paths forsignals or reflections from an object at a slightly different time. Invarious implementations, the radiating elements 402 are meta-structuresor patches in an array configuration such as in a 48-element antenna.The phase and amplification modules 404 provide phase shifting to alignthe signals in time. The radiating elements 402 are coupled to thecombination structure 406 and to phase and amplification modules 404,including phase shifters and LNAs implemented as PS circuits 320-324 andLNAs 338-342 of FIG. 3. In the present illustration, two objects, objectA 408 and object B 410, are located at a same range and having a samevelocity with respect to the antenna 400. When the distance between theobjects is less than the bandwidth of a radiation beam, the objects maybe indistinguishable by the system. This is referred to as angularresolution or spatial resolution. In the radar and object detectionfields, the angular resolution describes the radar's ability todistinguish between objects positioned proximate each other, whereinproximate location is generally measured by the range from an objectdetection mechanism, such as a radar antenna, to the objects and thevelocity of the objects.

Radar angular resolution is the minimum distance between two equallylarge targets at the same range which the radar can distinguish andseparate. The angular resolution is a function of the antenna'shalf-power beam width, referred to as the 3 dB beam width and serves aslimiting factor to object differentiation. Distinguishing objects isbased on accurately identifying the angle of arrival of reflections fromthe objects. Smaller beam width angles result in high directivity andmore refined angular resolution but requires faster scanning to achievethe smaller step sizes. For example, in autonomous vehicle applications,the radar is tasked with scanning an environment of the vehicle within asufficient time period for the vehicle to take corrective action whenneeded. This limits the capability of a system to specific steps. Thismeans that any object having a distance therebetween less than the 3 dBangle beam width cannot be distinguished without additional processing.Put another way, two identical targets at the same distance are resolvedin angle if they are separated by more than the antenna 3 dB beam width.The present examples use the multiple guard band antennas to distinguishbetween the objects.

FIG. 5 illustrates a radar signal and its associated scan parameters inmore detail. Radar signal 500 is an FMCW signal containing a series ofchirps, such as chirps 502-506. Signal 500 is defined by a set ofparameters that impact how to determine an object's location, itsresolution, and velocity. The parameters associated with the signal 500and illustrated in FIG. 5 include the following: (1) f_(max) and f_(min)for the minimum and maximum frequency of the chirp signal; (2) T_(total)for the total time for one chirp sequence; (3) T_(delay) representingthe settling time for a Phase Locked Loop (“PLL”) in the radar system;(4) T_(meas) for the actual measurement time (e.g., >2 s for a chirpsequence to detect objects within 300 meters); (5) T_(chip) for thetotal time of one chirp; (6) B for the total bandwidth of the chirp; (7)B_(eff) for the effective bandwidth of the chirp; (8) ΔB_(eff) for thebandwidth between consecutive measurements; (9) N_(r) for the number ofmeasurements taken per chirp (i.e., for each chirp, how manymeasurements will be taken of echoes); and (10) N_(c), the number ofchirps.

The distance and distance resolution of an object are fully determinedby the chirp parameters N_(r) and B_(eff). In some aspects, the rangeresolution can be expressed as follows:

$\begin{matrix}{{\Delta R} = {\frac{c}{2B_{eff}} \propto \frac{1}{B_{eff}}}} & ( {{Eq}.\mspace{14mu} 1} )\end{matrix}$

In some aspects, the maximum distance (or range) can be expressed asfollows:

$\begin{matrix}{R_{\max} = {{\frac{c}{4B_{eff}}N_{r}} \propto \frac{1}{\Delta B_{eff}}}} & ( {{Eq}.\mspace{14mu} 2} )\end{matrix}$

The velocity and velocity resolution of an object are fully determinedby chirp sequence parameters (N_(c), T_(chirp)) and frequency (f_(min)).The minimum velocity (or velocity resolution) achieved is determined asfollows (with c denoting the speed of light):

$\begin{matrix}{v_{\min} = {{\Delta \; v} = {{\frac{c}{2f_{c}}\frac{1}{N_{S}T_{chirp}}} \propto \frac{1}{T_{tot}}}}} & ( {{Eq}.\mspace{14mu} 3} )\end{matrix}$

Note that higher radar frequencies result in a better velocityresolution for the same sequence parameters. The maximum velocity isgiven by:

$\begin{matrix}{v_{\max} = {{\frac{c}{4f_{c}}\frac{1}{T_{chirp}}} \propto \frac{1}{T_{chirp}} \propto \frac{\Delta R}{R_{\max}}}} & ( {{Eq}.\mspace{14mu} 4} )\end{matrix}$

Additional relationships between the scan parameters are given by thefollowing equations, with Eq. 5 denoting the chirp slope κ_(chirp), andEq. 6 denoting the sample frequency:

$\begin{matrix}{\kappa_{chirp} = \frac{B_{eff}}{T_{chirp}}} & ( {{Eq}.\mspace{14mu} 5} ) \\{f_{sample} \propto {\kappa_{chirp}*R_{\max}}} & ( {{Eq}.\mspace{14mu} 6} )\end{matrix}$

In various aspects, the sample frequency is fixed. Also, the sample ratef_(sample) in Eq. 6 determines how fine a range resolution can beachieved for a selected maximum velocity and selected maximum range. Insome aspects, the maximum range R_(max) may be defined by a userconfiguration depending on the type of environment (or type of path)detected. Note that once the maximum range R_(max) is fixed, v_(max) andΔR are no longer independent. One chirp sequence or segment has multiplechirps. Each chirp is sampled multiple times to give multiple rangemeasurements and measure doppler velocity accurately. Each chirp may bedefined by its slope, κ_(chirp). The maximum range requirement may beinversely proportional to effective bandwidth of the chirp B_(eff) asindicated in Eq. 1, where an increase in the B_(eff) parameter canachieve an improved range resolution (or decreased range resolutionvalue). The decreased range resolution value may be useful for objectclassification in a city street environment, where objects are moving ata significantly lower velocity compared to the highway environment so animprovement in the range resolution parameter value bears more weightthan observing a degradation in the maximum velocity parameter.Similarly, the maximum velocity capability of a radar may be inverselyproportional to the chirp time T_(chirp) as indicated in Eq. 4, where adecrease in the T_(chirp) parameter can achieve an improved maximumvelocity (or increased maximum velocity value). The increased maximumvelocity may be useful for object detection in a highway environment,where objects are moving at a significantly higher velocity compared tothe city street environment so an improvement in the maximum velocityparameter bears more weight than observing a degradation in the rangeresolution parameter.

Note also that Eqs. 1-6 above can be used to establish scan parametersfor given design goals. For example, to detect objects at highresolution at long ranges, the radar system 300 needs to take a largenumber of measurements per chirp. If the goal is to detect objects athigh speed at a long range, the chirp time has to be low, limiting thechirp time. In the first case, high resolution detection at long rangeis limited by the bandwidth of the signal processing unit in the radarsystem. And in the second case, high maximum velocity at long range islimited by the data acquisition speed of the radar chipset (which alsolimits resolution).

Accordingly, in a LRR mode, the radar can start with a low number ofchirps to detect objects at long range in an initial scan. Once theobjects are detected, the radar adjusts its LRR mode to increase thenumber of chirps in the radar signal and improve the velocity estimationfor the detected objects. FIG. 6 illustrates this process. First, theradar initiates transmission of a scan in LRR mode with a reduced numberof chirps (600). The number of chirps may be, in some examples, as lowas 5 chirps. Once an echo is received (602) and objects are detected(604), the radar then rescans the FoV with a higher number of chirps tofocus beams at the range bins where the objects were detected (606).This enables the radar to extract both range and velocity for allobjects detected at long range (608) so that the objects can then beclassified by the perception engine 304 of FIG. 3 into the differenttypes of objects such as vehicles, cyclists, pedestrians, infrastructureobjects, animals, and so forth. The object classification is then sentto a sensor fusion module, where it is analyzed together with objectdetection information from other sensors such as lidar and camerasensors.

FIGS. 7A-B illustrate an example radar beam that is transmitted by theradar 300 with a narrow main beam 700 capable to reach a long range of300 m and side lobes that may be reduced with the guard antennas 310 and314 DSP processing in the DSP module 356 of FIG. 3. FIGS. 8A-Billustrate example scan parameter values 800-802 that may be implementedto achieve narrow beams such as beam 700 of FIGS. 7A-B. These parametersinclude an azimuth FoV of 44° with a steering beam width of 2°, a stepsize of 1 indicating approximately 22 steps for a complete scan of theFoV and a chirp time of 18 s. The total chirp segment time for 128chirps is about 4.2 ms for a complete scan time of the FoV ofapproximately 93 ms. The maximum range achieved is around 330 m for arange resolution of 1.5 m. And the max velocity is 30 m/s for a velocityresolution of 0.5 m/s. Note that the scan parameters in FIGS. 8A-B areexample parameters only; other parameters may be used in alternateimplementations.

FIG. 9 shows the range-speed velocity pattern and frequency spectrum fora received echo with the scan parameters of FIGS. 7A-B. Note that anobject 904 is seen at the range-doppler map 900 and in the peak 906 ofthe frequency spectrum 902 with an accurate velocity estimation with thevelocity resolution of 0.5 m/s. If the number of chirps is reduced in aninitial scan of the LRR mode as described in FIG. 6, the velocityestimation is degraded but the object can still be detected. This isseen in FIG. 10, with graph 1000 illustrating the object detected with64 chirps, graph 1002 illustrating the object detected with 20 chirps,and graph 1004 illustrating the object detected with 5 chirps. Thevelocity estimation for the object gets worse with a reduced number ofchirps as expected. However, the range estimation is still fairlyaccurate with even 5 chirps, enabling the radar to detect objectssignificantly faster and then adjust the scan to accurately estimate thevelocity of the detected objects. Note that there is no significantdifference in the return signal SNR between 5 chirps or 128 chirps, asillustrated in graphs 1100-1102 in FIG. 11. This is critical invalidating the use of fewer chirps in a LRR mode.

These various examples support autonomous driving with improved sensorperformance, all-weather/all-condition detection, advanceddecision-making algorithms and interaction with other sensors throughsensor fusion. These configurations optimize the use of radar sensors,as radar is not inhibited by weather conditions in many applications,such as for self-driving cars. The radar described here is effectively a“digital eye,” having true 3D vision and capable of human-likeinterpretation of the world.

It is appreciated that the previous description of the disclosedexamples is provided to enable any person skilled in the art to make oruse the present disclosure. Various modifications to these examples willbe readily apparent to those skilled in the art, and the genericprinciples defined herein may be applied to other examples withoutdeparting from the spirit or scope of the disclosure. Thus, the presentdisclosure is not intended to be limited to the examples shown hereinbut is to be accorded the widest scope consistent with the principlesand novel features disclosed herein.

As used herein, the phrase “at least one of” preceding a series ofitems, with the terms “and” or “or” to separate any of the items,modifies the list as a whole, rather than each member of the list (i.e.,each item). The phrase “at least one of” does not require selection ofat least one item; rather, the phrase allows a meaning that includes atleast one of any one of the items, and/or at least one of anycombination of the items, and/or at least one of each of the items. Byway of example, the phrases “at least one of A, B, and C” or “at leastone of A, B, or C” each refer to only A, only B, or only C; anycombination of A, B, and C; and/or at least one of each of A, B, and C.

Furthermore, to the extent that the term “include,” “have,” or the likeis used in the description or the claims, such term is intended to beinclusive in a manner similar to the term “comprise” as “comprise” isinterpreted when employed as a transitional word in a claim.

A reference to an element in the singular is not intended to mean “oneand only one” unless specifically stated, but rather “one or more.” Theterm “some” refers to one or more. Underlined and/or italicized headingsand subheadings are used for convenience only, do not limit the subjecttechnology, and are not referred to in connection with theinterpretation of the description of the subject technology. Allstructural and functional equivalents to the elements of the variousconfigurations described throughout this disclosure that are known orlater come to be known to those of ordinary skill in the art areexpressly incorporated herein by reference and intended to beencompassed by the subject technology. Moreover, nothing disclosedherein is intended to be dedicated to the public regardless of whethersuch disclosure is explicitly recited in the above description.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of what may be claimed, but ratheras descriptions of particular implementations of the subject matter.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable sub combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to a subcombination or variation of a sub combination.

The subject matter of this specification has been described in terms ofparticular aspects, but other aspects can be implemented and are withinthe scope of the following claims. For example, while operations aredepicted in the drawings in a particular order, this should not beunderstood as requiring that such operations be performed in theparticular order shown or in sequential order, or that all illustratedoperations be performed, to achieve desirable results. The actionsrecited in the claims can be performed in a different order and stillachieve desirable results. As one example, the processes depicted in theaccompanying figures do not necessarily require the particular ordershown, or sequential order, to achieve desirable results. Moreover, theseparation of various system components in the aspects described aboveshould not be understood as requiring such separation in all aspects,and it should be understood that the described program components andsystems can generally be integrated together in a single hardwareproduct or packaged into multiple hardware products. Other variationsare within the scope of the following claim.

What is claimed is:
 1. A beam steering radar for use in an autonomousvehicle, comprising: a radar module, comprising: at least one beamsteering antenna; a transceiver; and a controller configured to causethe transceiver to perform, using the at least one beam steeringantenna, a first scan of a field-of-view (FoV) with a first number ofchirps in a first radio frequency (RF) signal and a second scan of theFoV with a second number of chirps different from the first number ofchirps in a second RF signal; and a perception module comprising amachine learning-trained classifier configured to detect one or moreobjects in a path and surrounding environment of the autonomous vehiclebased on the first number of chirps in the first RF signal and classifythe one or more objects based on the second number of chirps in thesecond RF signal, wherein the perception module is configured totransmit object data and radar control information to the radar module.2. The beam steering radar of claim 1, wherein the second number ofchirps is greater than the first number of chirps.
 3. The beam steeringradar of claim 1, wherein the controller is further configured todetermine a velocity resolution of the one or more objects from theobject data, wherein the velocity resolution is inversely proportionalto a total time for a chirp sequence.
 4. The beam steering radar ofclaim 1, wherein the controller is further configured to obtain a firstvelocity resolution of the one or more objects from the object data thatcorresponds to the first number of chirps in the first RF signal andobtain a second velocity resolution different from the first velocityresolution of the one or more objects from the object data thatcorresponds to the second number of chirps in the second RF signal. 5.The beam steering radar of claim 4, wherein the second velocityresolution is lesser than the first velocity resolution.
 6. The beamsteering radar of claim 1, wherein the perception module is configuredto detect the one or more objects in a first duration from radar datathat corresponds to the first number of chirps in the first RF signaland classify the one or more objects in a second duration different fromthe first duration from radar data that corresponds to the second numberof chirps in the second RF signal.
 7. The beam steering radar of claim6, wherein the second duration is greater than the first duration. 8.The beam steering radar of claim 1, wherein the second RF signal istransmitted through the at least one beam steering antenna at a timesubsequent to that of a transmission of the first RF signal.
 9. The beamsteering radar of claim 1, wherein the transceiver is configured toperform the first scan of the FoV at a first beam scanning rate based onthe first number of chirps in the first RF signal and perform the secondscan of the FoV at a second beam scanning rate different from the firstbeam scanning rate based on the second number of chirps in the second RFsignal.
 10. The beam steering radar of claim 9, wherein the first beamscanning rate is greater than the second beam scanning rate.
 11. Thebeam steering radar of claim 1, wherein the controller is furtherconfigured to cause the transceiver to perform the first scan and thesecond scan based on a set of scan parameters that is adjustable toproduce a plurality of transmission beams through the at least one beamsteering antenna.
 12. The beam steering radar of claim 11, wherein theset of scan parameters includes one or more of a total angle of a scanarea defining the FoV, a beam width of each of the plurality oftransmission beams, a scan angle of each of the plurality oftransmission beams, indication of the first number of chirps in thefirst RF signal, indication of the second number of chirps in the secondRF signal, a chirp time, a chirp segment time, or a chirp slope.
 13. Amethod of object detection and classification, comprising: transmitting,at a transceiver using at least one beam steering antenna, a firsttransmission beam comprising a first number of chirps at a first time;receiving, at the transceiver through the at least one beam steeringantenna, a first reflected signal associated with the first transmissionbeam; detecting, using a perception module, an object in the firstreflected signal based on the first number of chirps in the firsttransmission beam; transmitting, at the transceiver using the at leastone beam steering antenna, a second transmission beam comprising asecond number of chirps greater than the first number of chirps at asecond time subsequent to the first time; and classifying, using theperception module, the object from a second reflected signal associatedwith the second transmission beam based on the second number of chirpsin the second transmission beam.
 14. The method of claim 13, wherein theclassifying the object comprises calculating a velocity resolution ofthe object from object data, wherein the velocity resolution isinversely proportional to a total time for a chirp sequence.
 15. Themethod of claim 13, wherein the classifying the object comprisescalculating a first velocity resolution of the object from object datathat corresponds to the first number of chirps in the first transmissionbeam and obtain a second velocity resolution lesser than the firstvelocity resolution of the object from object data that corresponds tothe second number of chirps in the second transmission beam.
 16. Themethod of claim 13, wherein: the detecting the object comprisesdetecting the object in a first duration from radar data thatcorresponds to the first number of chirps in the first transmissionbeam, and the classifying the object comprises classifying the object ina second duration greater than the first duration from radar data thatcorresponds to the second number of chirps in the second transmissionbeam.
 17. The method of claim 13, wherein: the transmitting the firsttransmission beam comprises performing a first scan of a field-of-view(FoV) at a first beam scanning rate based on the first number of chirpsin the first transmission beam, and the transmitting the secondtransmission beam comprises performing a second scan of the FoV at asecond beam scanning rate greater than the first beam scanning ratebased on the second number of chirps in the second transmission beam.18. The method of claim 17, wherein the first scan and the second scanare performed based on a set of scan parameters that is adjustable toproduce a plurality of transmission beams through the at least one beamsteering antenna, wherein the set of scan parameters includes one ormore of a total angle of a scan area defining the FoV, a beam width ofeach of the plurality of transmission beams, a scan angle of each of theplurality of transmission beams, indication of the first number ofchirps in the first transmission beam, indication of the second numberof chirps in the second transmission beam, a chirp time, a chirp segmenttime, or a chirp slope.
 19. An autonomous driving system, comprising: anon-transitory memory; and one or more hardware processors coupled tothe non-transitory memory and configured to execute instructions fromthe non-transitory memory to cause the autonomous driving system toperform operations comprising: performing a first scan of afield-of-view (FoV) at a first beam scanning rate using a first numberof chirps in a first transmission beam; detecting an object in a firstreceived reflected signal based on the first number of chirps in thefirst transmission beam; performing a second scan of the FoV at a secondbeam scanning rate greater than the first beam scanning rate using asecond number of chirps different from the first number of chirps in asecond transmission beam; and classifying the object from a secondreceived reflected signal associated with the second transmission beambased on the second number of chirps in the second transmission beam.20. The autonomous driving system of claim 19, wherein: the first numberof chirps is lesser than the second number of chirps, and the secondbeam scanning rate is lesser than the first beam scanning rate.